#coding:utf-8

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

#自动下载并导入数据
mnist=input_data.read_data_sets('./datas/mnist/',one_hot=True)

# Hyper-parameters，超参数
learning_rate = 0.001
num_steps = 500
batch_size = 128
display_step = 10

# Network Parameters，网络参数
num_input = 784 # MNIST数据输入 (img shape: 28*28)
num_classes = 10 # MNIST所有类别 (0-9 digits)
dropout = 0.75 # Dropout, probability to keep units，保留神经元相应的概率

# tf Graph input，TensorFlow图结构输入
X = tf.placeholder(tf.float32, [None, num_input])
Y = tf.placeholder(tf.float32, [None, num_classes])
keep_prob = tf.placeholder(tf.float32) # dropout (keep probability)，保留i

# Create some wrappers for simplicity，创建基础卷积函数，简化写法
def conv2d(x,W,b,stride=1):
    # Conv2D wrapper, with bias and relu activation，卷积层，包含bias与非线性relu激励
    x=tf.nn.conv2d(x,W,strides=[1,stride,stride,1],padding='SAME')
    x=tf.nn.bias_add(x,b)
    return tf.nn.relu(x)

def maxpool2d(x,k=2):
    # MaxPool2D wrapper，最大池化层
    return tf.nn.max_pool(x,ksize=[1,k,k,1],strides=[1,k,k,1],padding='SAME')

 # Create model，创建模型
def create_cnn(x, weights, biases, dropout):
    # MNIST数据为维度为1，长度为784 (28*28 像素)的
    # Reshape to match picture format [Height x Width x Channel]
    # Tensor input become 4-D: [Batch Size, Height, Width, Channel]
    x=tf.reshape(x,shape=[-1,28,28,1])

    # Convolution Layer，卷积层
    conv1 = conv2d(x, weights['wc1'], biases['bc1'])
    # Max Pooling (down-sampling)，最大池化层／下采样
    conv1 = maxpool2d(conv1, k=2)

    # Convolution Layer，卷积层
    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
    # Max Pooling (down-sampling)，最大池化层／下采样
    conv2 = maxpool2d(conv2, k=2)

    # Fully connected layer，全连接网络
    # Reshape conv2 output to fit fully connected layer input，调整conv2层输出的结果以符合全连接层的需求
    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
    fc1 = tf.nn.relu(fc1)
    # Apply Dropout，应用dropout
    fc1 = tf.nn.dropout(fc1, dropout)

    # Output, class prediction，最后输出预测
    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
    return out

# Store layers weight & bias 存储每一层的权值和全差
weights = {
    # 5x5 conv, 1 input, 32 outputs
    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
    # 5x5 conv, 32 inputs, 64 outputs
    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
    # fully connected, 7*7*64 inputs, 1024 outputs
    'wd1': tf.Variable(tf.random_normal([7*7*64, 1024])),
    # 1024 inputs, 10 outputs (class prediction)
    'out': tf.Variable(tf.random_normal([1024, num_classes]))
}

biases = {
    'bc1': tf.Variable(tf.random_normal([32])),
    'bc2': tf.Variable(tf.random_normal([64])),
    'bd1': tf.Variable(tf.random_normal([1024])),
    'out': tf.Variable(tf.random_normal([num_classes]))
}

# Construct model，构建模型
logits = create_cnn(X, weights, biases, keep_prob)
prediction=tf.nn.softmax(logits)

# Define loss and optimizer，定义误差函数与优化器
loss_op=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Y,logits=logits))
optomizer=tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op=optomizer.minimize(loss_op)

## Evaluate model，评估模型
correct_pred=tf.equal(tf.argmax(prediction,1),tf.argmax(Y,1))
accuracy=tf.reduce_mean(tf.cast(correct_pred,tf.float32))

init=tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)

    for step in range(1,num_steps+1):
        batch_x,batch_y=mnist.train.next_batch(batch_size)
        # Run optimization op (backprop)，优化
        sess.run(train_op,feed_dict={X:batch_x,Y:batch_y,keep_prob: dropout})
        if step % display_step == 0 or step == 1:
            # Calculate batch loss and accuracy
            loss, acc = sess.run([loss_op, accuracy], feed_dict={X: batch_x,
                                                                 Y: batch_y,
                                                                 keep_prob: 1.0})
            print("Step " + str(step) + ", Minibatch Loss= " + \
                  "{:.4f}".format(loss) + ", Training Accuracy= " + \
                  "{:.3f}".format(acc))

    print("Optimization Finished!")
    # Calculate accuracy for 256 MNIST test images，以每256个测试图像为例，
    print("Testing Accuracy:", \
        sess.run(accuracy, feed_dict={X: mnist.test.images[:256],
                                      Y: mnist.test.labels[:256],
                                      keep_prob: 1.0}))